programming noob here. I'm trying to use a matplotlib widget in a PyQt4 GUI. The widget is similar to matplotlib's example for qt.
At some point the user needs to click on the plot, which I thought something like ginput() would handle. However, this doesn't work because the figure doesn't have a manager (see below). Note that this is very similar to another question but it never got answered.
AttributeError: 'NoneType' object has no attribute 'manager'
Figure.show works only for figures managed by pyplot, normally created by pyplot.figure().
I'm assuming by "normally" there's a way around this.
Another simple script to demonstrate:
from __future__ import print_function
from matplotlib.figure import Figure
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 5, 0.1)
y = np.sin(x)
# figure creation by plt (also given a manager, although not explicitly)
plt.figure()
plt.plot(x,y)
coords = plt.ginput() # click on the axes somewhere; this works
print(coords)
# figure creation w/o plt
manualfig = Figure()
manualaxes = manualfig.add_subplot(111)
manualaxes.plot(x,y)
manualfig.show() # will fail because of no manager, yet shown as a method
manualcoords = manualfig.ginput() # comment out above and this fails too
print(manualcoords)
As popular as pyplot is (I can't hardly find an answer without it), it doesn't seem to play nice when working with a GUI. I thought pyplot was simply a wrapper for the OO framework but I guess I'm just a noob.
My question then is this:
Is there some way to attach pyplot to an instance of matplotlib.figure.Figure?
Is there an easy way to attach a manager to a Figure? I found new_figure_manager() in matplotlib.backends.backend_qt4agg, but couldn't get it to work, even if it is the right solution.
Many thanks,
James
pyplot is just a wrapper for the OO interface, but it does a lot of work for you read the example you link to again carefully, the
FigureCanvas.__init__(self, fig)
line is very important as that is what tells the figure what canvas to use. The Figure object is just a collection of Axes objects (and a few Text objects), the canvas object is what knows how to turn Artist objects (ie matplotlib's internal representation of lines, text, points, etc) in to pretty colors. Also see something I wrote for another embedding example which does not sub-class FigureCanvas.
There is a PR to make this process easier, but it is stalled while we get 1.4 out the door.
also see: Which is the recommended way to plot: matplotlib or pylab?, How can I attach a pyplot function to a figure instance?
Related
TL;DR:
I would like to output a matplotlib.figure.Figure instance with matplotlib.pyplot.
Long version:
I have a function that returns a matplotlib.figure.Figure instance. The function adds some data to the figure, but does not yet show the graph. Thus, it is possible to decide afterwards what backend should be used to output the figure. I was able to find out how to do draw the graph with tkinter, and it shouldn't be much of a problem to do the same with other backends. But, and that is my problem, I couldn't find out how to draw the figure with a simple pyplot command. How should I do this?
I'm not really new to matplotlib and I'm deeply ashamed to admit I have always used it as a tool for getting a solution as quick and easy as possible. So I know how to get basic plots, subplots and stuff and have quite a few code which gets reused from time to time...but I have no "deep(er) knowledge" of matplotlib.
Recently I thought I should change this and work myself through some tutorials. However, I am still confused about matplotlibs plt, fig(ure) and ax(arr). What is really the difference?
In most cases, for some "quick'n'dirty' plotting I see people using just pyplot as plt and directly plot with plt.plot. Since I am having multiple stuff to plot quite often, I frequently use f, axarr = plt.subplots()...but most times you see only code putting data into the axarr and ignoring the figure f.
So, my question is: what is a clean way to work with matplotlib? When to use plt only, what is or what should a figure be used for? Should subplots just containing data? Or is it valid and good practice to everything like styling, clearing a plot, ..., inside of subplots?
I hope this is not to wide-ranging. Basically I am asking for some advice for the true purposes of plt <-> fig <-> ax(arr) (and when/how to use them properly).
Tutorials would also be welcome. The matplotlib documentation is rather confusing to me. When one searches something really specific, like rescaling a legend, different plot markers and colors and so on the official documentation is really precise but rather general information is not that good in my opinion. Too much different examples, no real explanations of the purposes...looks more or less like a big listing of all possible API methods and arguments.
pyplot is the 'scripting' level API in matplotlib (its highest level API to do a lot with matplotlib). It allows you to use matplotlib using a procedural interface in a similar way as you can do it with Matlab. pyplot has a notion of 'current figure' and 'current axes' that all the functions delegate to (#tacaswell dixit). So, when you use the functions available on the module pyplot you are plotting to the 'current figure' and 'current axes'.
If you want 'fine-grain' control of where/what your are plotting then you should use an object oriented API using instances of Figure and Axes.
Functions available in pyplot have an equivalent method in the Axes.
From the repo anatomy of matplotlib:
The Figure is the top-level container in this hierarchy. It is the overall window/page that everything is drawn on. You can have multiple independent figures and Figures can contain multiple Axes.
But...
Most plotting occurs on an Axes. The axes is effectively the area that we plot data on and any ticks/labels/etc associated with it. Usually we'll set up an Axes with a call to subplot (which places Axes on a regular grid), so in most cases, Axes and Subplot are synonymous.
Each Axes has an XAxis and a YAxis. These contain the ticks, tick locations, labels, etc.
If you want to know the anatomy of a plot you can visit this link.
I think that this tutorial explains well the basic notions of the object hierarchy of matplotlib like Figure and Axes, as well as the notion of current figure and current Axes.
If you want a quick answer: There is the Figure object which is the container that wraps multiple Axes(which is different from axis) which also contains smaller objects like legends, line, tick marks ... as shown in this image taken from matplotlib documentation
So when we do
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> type(fig)
<class 'matplotlib.figure.Figure'>
>>> type(ax)
<class 'matplotlib.axes._subplots.AxesSubplot'>
We have created a Figure object and an Axes object that is contained in that figure.
pyplot is matlab like API for those who are familiar with matlab and want to make quick and dirty plots
figure is object-oriented API for those who doesn't care about matlab style plotting
So you can use either one but perhaps not both together.
I am interested in making movies using matplotlib. Examples I've seen so far, such as this one for moviewriter, seem to have you editing the data in-place for each frame. This is very efficient, avoiding redrawing the parts of the image that stay the same each time. However, it can be clunky for rapid data exploration. I would like a recipe that lets me simply take a fully drawn figure as each frame (clearing the same figure object each time is fine).
The reason for this: I often create moderately complicated figures using custom functions, with a form like plotme(ax, data, **options). Often I develop these functions without animations in mind, and later want to animate the figures by calling the plotting function in a loop. I don't want to have to change the logic of the functions to "setData" of existing artists in the figure for each frame.
Although the example code you've shown updates existing plot objects, there is no reason that you need to do so. The critical part of the attached code is the writer.grab_frame() which simply gets a screen capture of the current figure.
Here is an example without using existing plot objects
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.animation as manimation
FFMpegWriter = manimation.writers['ffmpeg']
metadata = dict(title='Movie Test', artist='Matplotlib',
comment='Movie support!')
writer = FFMpegWriter(fps=15, metadata=metadata)
fig = plt.figure()
with writer.saving(fig, "writer_test.mp4", 100):
for k in range(10):
# Create a new plot object
plt.plot(range(k), range(k), 'o')
writer.grab_frame()
I'm asking this question because I can't solve one problem in Python/Django (actually in pure Python it's ok) which leads to RuntimeError: tcl_asyncdelete async handler deleted by the wrong thread. This is somehow related to the way how I render matplotlib plots in Django. The way I do it is:
...
import matplotlib.pyplot as plt
...
fig = plt.figure()
...
plt.close()
I extremely minimized my code. But the catch is - even if I have just one line of code:
fig = plt.figure()
I see this RuntimeError happening. I hope I could solve the problem, If I knew the correct way of closing/cleaning/destroying plots in Python/Django.
By default matplotlib uses TK gui toolkit, when you're rendering an image without using the toolkit (i.e. into a file or a string), matplotlib still instantiates a window that doesn't get displayed, causing all kinds of problems. In order to avoid that, you should use an Agg backend. It can be activated like so --
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot
For more information please refer to matplotlib documentation -- http://matplotlib.org/faq/howto_faq.html#matplotlib-in-a-web-application-server
The above (accepted) answer is a solution in a terminal environment. If you debug in an IDE, you still might wanna use 'TkAgg' for displaying data. In order to prevent this issue, apply these two simple rules:
everytime you display your data, initiate a new fig = plt.figure()
don't close old figures manually (e.g. when using a debug mode)
Example code:
import matplotlib
matplotlib.use('TkAgg')
from matplotlib import pyplot as plt
fig = plt.figure()
plt.plot(data[:,:,:3])
plt.show()
This proves to be the a good intermediate solution under MacOS and PyCharm IDE.
If you don't need to show plots while debugging, the following works:
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
However, if you would like to plot while debugging, you need to do 3 steps:
1.Keep backend to 'TKAgg' as follows:
import matplotlib
matplotlib.use('TKAgg')
from matplot.lib import pyplot as plt
or simply
import matplotlib.pyplot as plt
2.As Fábio also mentioned, you need to add fig(no. #i)=plt.figure(no.#i) for each figure #i. As the following example for plot no.#1, add:
fig1 = plt.figure(1)
plt.plot(yourX,yourY)
plt.show()
3.Add breakpoints. You need to add two breakpoints at least, one somewhere at the beginning of your codes (before the first plot), and the other breakpoint at a point where you would like all plots (before to the second breakpoint) are plotted. All figures are plotted and you even don't need to close any figure manually.
For me, this happened due to parallel access to data by both Matplotlib and by Tensorboard, after Tensorboard's server was running for a week straight.
Rebotting tensorboard tensorboard --logdir . --samples_per_plugin images=100 solved this for me.
I encountered this problem when plotting graphs live with matplotlib in my tkinter application.
The easiest solution I found, was to always delete subplots. I found you didn't need to instantiate a new figure, you only needed to delete the old subplot (using del subplot), then remake it.
Before plotting a new graph, make sure to delete the old subplot.
Example:
f = Figure(figsize=(5,5), dpi=100)
a = f.add_subplot(111)
(For Loop code that updates graph every 5 seconds):
del a #delete subplot
a = f.add_subplot(111) #redefine subplot
Finding this simple solution to fix this "async handler bug" was excruciatingly painful, I hope this helps someone else :)
I am trying to understand how methods and attributes are organized in matplotlib. For example, say I have a figure:
import matplotlib.pyplot as plt
my_fig = plt.imshow(image)
I have noticed that some figure properties are set via module methods, e.g.:
plt.axis('off')
while others are set for the figure itself using object methods:
my_fig.set_cmap('hot')
Can figure properties be specified in either way?
How can I turn off the axis by calling methods on my object my_fig?
The plt methods are part of the pyplot API, which is intended to provide Matlab-like convenience for interactive use (and certainly appears to be very influenced by Matlab). But it's just one small facet of the whole matplotlib API (which is much more OOP). In practice I seem to end up mixing them both myself in SW; it's largely a matter of taste whether you go through the pyplot API or access the objects. pyplot is certainly very convenient although as you want to do more complex/exotic things you'll find what you can do with pyplot alone limited and you'll need to get to know at least the full API's Axes, Figure, Legend and Path objects better.
Pyplot is a collection of command style functions that make matplotlib work like MATLAB, matplotlib.figure.Figure is part of the object-oriented API.
In most cases you can configure figure settings via itself like this:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
image=mpimg.imread('stinkbug.png')
my_fig = plt.imshow(image)
my_fig.axes.axes.get_xaxis().set_visible(False)
my_fig.axes.axes.get_yaxis().set_visible(False)
plt.show()
enter code here
required stinkbug.png:
result: